Content area
As coastal tidal flats—ecosystems of high ecological significance and socio-economic value—face accelerating degradation driven by climate change and intensified anthropogenic disturbances, there is an urgent need for efficient, automated, and scalable monitoring solutions. Traditional monitoring approaches are constrained by high implementation costs and limited spatial coverage, whereas remote sensing—particularly multispectral satellite imagery such as Sentinel-2—has emerged as a primary and widely adopted tool for large-scale environmental observation. Building upon recent advancements in cloud computing and WebGIS technologies, this study presents a web-based, interactive tidal flat extraction system implemented on Alibaba’s AI Earth platform. The system integrates multiple water indices (NDWI, mNDWI, and IWI) with a machine learning algorithm (Random Forest), and is deployed through a user-friendly interface developed using Vue.js and Leaflet, enabling flexible parameter configuration and real-time visualization of extraction results. Its front-end/back-end decoupled architecture enables non-programming users to conduct large-scale tidal flat mapping, thereby substantially lowering the technical barriers to coastal tidal flat monitoring and management in China.
Details
Datasets;
Algorithms;
Environmental monitoring;
Satellite imagery;
Remote sensing;
Environmental restoration;
Architecture;
Machine learning;
Monitoring;
Tidal flats;
Ecosystems;
Visualization;
Wetlands;
Distributed processing;
Aerial photography;
Infrastructure;
Shoreline protection;
Cloud computing;
Classification;
Archives & records;
Remote sensing systems;
Integrated approach;
Real time
1 College of Computer Science and Technology, Hangzhou Normal University, Hangzhou 311121, China; [email protected] (S.S.); [email protected] (Q.S.); [email protected] (H.L.); [email protected] (Z.Y.); [email protected] (P.C.)
2 College of Computer Science and Technology, Hangzhou Normal University, Hangzhou 311121, China; [email protected] (S.S.); [email protected] (Q.S.); [email protected] (H.L.); [email protected] (Z.Y.); [email protected] (P.C.), Institute of Remote Sensing and Earth Sciences, Hangzhou Normal University, Hangzhou 311121, China
3 School of Engineering, Hangzhou Normal University, Hangzhou 311121, China; [email protected]
4 Institute of Remote Sensing and Earth Sciences, Hangzhou Normal University, Hangzhou 311121, China, School of Engineering, Hangzhou Normal University, Hangzhou 311121, China; [email protected]